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train.py
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train.py
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import os
import torch
import torch.distributed as dist
from torch import optim
from torch.nn import parallel
from runx.logx import logx
from data import get_cross_domain_train_loader
from data import get_test_loader
from data import get_train_loader
from engine import get_trainer
from models.model import Model
def train(cfg):
# Training data loader
if not cfg.joint_training: # single domain
train_loader = get_train_loader(root=os.path.join(cfg.source.root, cfg.source.train),
batch_size=cfg.batch_size,
image_size=cfg.image_size,
random_flip=cfg.random_flip,
random_crop=cfg.random_crop,
random_erase=cfg.random_erase,
color_jitter=cfg.color_jitter,
padding=cfg.padding,
num_workers=4)
else: # cross domain
source_root = os.path.join(cfg.source.root, cfg.source.train)
target_root = os.path.join(cfg.target.root, cfg.target.train)
train_loader = get_cross_domain_train_loader(source_root=source_root,
target_root=target_root,
batch_size=cfg.batch_size,
random_flip=cfg.random_flip,
random_crop=cfg.random_crop,
random_erase=cfg.random_erase,
color_jitter=cfg.color_jitter,
padding=cfg.padding,
image_size=cfg.image_size,
num_workers=8)
# Evaluation data loader
query_loader = None
gallery_loader = None
if cfg.eval_interval > 0:
query_loader = get_test_loader(root=os.path.join(cfg.target.root, cfg.target.query),
batch_size=512,
image_size=cfg.image_size,
num_workers=4)
gallery_loader = get_test_loader(root=os.path.join(cfg.target.root, cfg.target.gallery),
batch_size=512,
image_size=cfg.image_size,
num_workers=4)
# Model
num_classes = cfg.source.num_id
cam_ids = train_loader.dataset.target_dataset.cam_ids if cfg.joint_training else train_loader.dataset.cam_ids
num_instances = len(train_loader.dataset.target_dataset) if cfg.joint_training else len(train_loader.dataset)
model = Model(num_classes=num_classes,
drop_last_stride=cfg.drop_last_stride,
joint_training=cfg.joint_training,
num_instances=num_instances,
cam_ids=cam_ids,
neg_proto=cfg.neg_proto,
neighbor_mode=cfg.neighbor_mode,
neighbor_eps=cfg.neighbor_eps,
threshold=cfg.threshold,
scale=cfg.scale,
mix_st=cfg.mix_st,
alpha=cfg.alpha,
nn_filter=cfg.nn_filter,
momentum=cfg.momentum,
loss_factor=cfg.loss_factor)
# ids=train_loader.dataset.target_dataset.ids)
model.cuda()
# Optimizer
ft_params = model.backbone.parameters()
new_params = [param for name, param in model.named_parameters() if not name.startswith("backbone.")]
param_groups = [{'params': ft_params, 'lr': cfg.ft_lr},
{'params': new_params, 'lr': cfg.new_params_lr}]
if cfg.optimizer_type == 'sgd':
optimizer = optim.SGD(param_groups, momentum=0.9, weight_decay=cfg.wd)
else:
optimizer = optim.Adam(param_groups, weight_decay=cfg.wd)
lr_scheduler = optim.lr_scheduler.MultiStepLR(optimizer=optimizer,
milestones=cfg.lr_step,
gamma=0.1)
# Convert model for mixed precision distributed training
if dist.is_initialized():
model = parallel.DistributedDataParallel(model, device_ids=[dist.get_rank()])
# Training engine
engine = get_trainer(model=model,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
enable_amp=cfg.fp16,
log_period=cfg.log_period,
save_interval=10,
eval_interval=cfg.eval_interval,
query_loader=query_loader,
gallery_loader=gallery_loader)
# training
engine.run(train_loader, max_epochs=cfg.num_epoch)
if dist.is_initialized():
dist.destroy_process_group()
if __name__ == '__main__':
import yaml
import argparse
import random
import numpy as np
from pprint import pformat
from yacs.config import CfgNode
from configs.default import strategy_cfg
from configs.default import dataset_cfg
parser = argparse.ArgumentParser()
parser.add_argument("--cfg", type=str, default="configs/cross_domain.yml")
parser.add_argument("--local_rank", type=int, default=None)
args = parser.parse_args()
# Initialize distributed training
num_gpus = torch.cuda.device_count()
if num_gpus > 1:
torch.distributed.init_process_group(backend="nccl", rank=args.local_rank, world_size=num_gpus)
if args.local_rank is not None:
torch.cuda.set_device(args.local_rank)
torch.backends.cudnn.benchmark = True
# Load configuration
customized_cfg = yaml.load(open(args.cfg, "r"), yaml.SafeLoader)
cfg = strategy_cfg
cfg.merge_from_file(args.cfg)
source_cfg = dataset_cfg.get(cfg.source_dataset)
target_cfg = dataset_cfg.get(cfg.target_dataset)
cfg.source = CfgNode()
cfg.target = CfgNode()
for k, v in source_cfg.items():
cfg.source[k] = v
for k, v in target_cfg.items():
cfg.target[k] = v
cfg.batch_size = cfg.batch_size // torch.cuda.device_count()
cfg.freeze()
# Set random seed
seed = 0 if args.local_rank is None else args.local_rank
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
# Set up logger
logx.initialize(logdir='logs/{}'.format(cfg.prefix), hparams=cfg, tensorboard=True,
global_rank=args.local_rank if dist.is_initialized() else 0)
logx.msg(pformat(cfg))
train(cfg)